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Combining Self-Regulation and Competence-Based Guidance to Personalise the Learning Experience in Moodle

Conference Paper

Combining Self-Regulation and Competence-Based Guidance to Personalise the Learning Experience in Moodle

Abstract and Figures

Adaptive learning systems aim to address a learner's specific needs, considering factors such as prior knowledge, learning efficiency, learning goals and motivation. Especially in distance education, often directed to adult learners with full-time jobs, it is very important to provide assistance to counteract high dropout rates. This paper describes an approach on how to support adult learners through the adoption of personalisation and guidance in Moodle. The implementation grounds on the combination of two pedagogical theories, competence-based learning and Self-Regulated Learning (SRL). Three-phases were used to roughly frame the design of the SRL learning flow, where the individual phases are supported by competence-based guidance. In this way Moodle is extended from a teacher and course management to a learner-centric system. This work has been implemented and evaluated in the course of a European project that targets vocational training of heat pump installers.
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Combining Self-regulation and Competence-based Guidance to Personalise the
Learning Experience in Moodle
Simone Kopeinik
Knowledge Technologies
Institute
Graz University of Technology
Graz, Austria
simone.kopeinik@tugraz.at
Alexander Nussbaumer
alexander.nussbaumer@tugraz.at
Lisa-Christina Winter
l.winter@tugraz.at
Dietrich Albert
Knowledge Technologies
Institute
Graz University of Technology,
Department of Psychology
University of Graz
Graz, Austria
dietrich.albert@tugraz.at
dietrich.albert@uni-graz.at
Aurora Dimache
Galway-Mayo Institute of
Technology
Galway, Ireland
aurora.dimache@gmit.ie
Thomas Roche
tom.roche@gmit.ie
Abstract: Adaptive learning systems aim to address a
learner’s specific needs, considering factors such as prior
knowledge, learning efficiency, learning goals and
motivation. Especially in distance education, often directed
to adult learners with full-time jobs, it is very important to
provide assistance to counteract high dropout rates. This
paper describes an approach on how to support adult
learners through the adoption of personalisation and
guidance in Moodle. The implementation grounds on the
combination of two pedagogical theories, competence-based
learning and Self-Regulated Learning (SRL). Three-phases
were used to roughly frame the design of the SRL learning
flow, where the individual phases are supported by
competence-based guidance. In this way Moodle is extended
from a teacher and course management to a learner-centric
system. This work has been imple mented and evaluated in
the course of a European project that targets vocational
training of heat pump installers.
Keywords: Personalised online training, Competence-
based Knowledge Space Theory, Self-Regulated Learning,
Learner modelling, Learning object recommendation, Moodle.
I. INTRODUCTION
Moodle (Modular Object-Oriented Dynamic Learning
Environment) is an open source Learning Management
System (LMS) that includes many features that improve
pedagogical quality [1], while assisting teachers and
course developers in creating and managing online
courses. It supports a variety of different learning object
(LO) formats and question types (herein referred to as
assessment items (AIs)). Despite these advantages,
Moodle is course-based and does not cater for the
individual needs of students [2]. However, its extensibility
(plug-ins and modules) offers a promising possibility to
introduce personalised learning support, which gives the
learner more freedom to control their own learning
process. In this paper we describe a personalisation
method developed within the scope of the INNOVRET
project (Innovative Online Vocational Training of
Renewable Energy Technologies) that aims to provide an
online training solution for heat pump installers. This
personalisation approach grounds on two learning
theories, Competence-based Knowledge Space Theory
(CbKST) [3] and Self-Regulated Learning (SRL) [7], that
are used as the underlying models for our technical
implementation. Following the ideas described in [5] and
[6] we have designed, implemented and integrated an
adaptive learning solution in Moodle.
II. PSYCHO-PEDAGOGICAL APPROACHES
A. Competence-based Learning
In the context of competence-based learning the
learning content is adapted based on the learner’s current
competence state. CbKST provides a theoretically sound
framework to model the competences of a learning
domain, prerequisite relations between competences and
associations of learning objects and assessment items with
competences. As described in [4], such domain models
are used to instantiate user models by assigning
competences to learners. Literature [3] covers methods to
adaptively assess the learner's competence state and to
recommend learning objects fitting to the current
competence state.
B. Self-Regulated Learning
While competence-based learning focuses on the
subject domain level, SRL emphasises the learners’ own
ability to control and regulate each step of their learning
process. A common SRL model regards learning as a
cyclic process divided into three phases [7]: (1)
Forethought (also referred to as planning phase), (2)
Performance (also referred to as learning phase), and (3)
Self-Reflection. In addition to acquiring domain
knowledge, the learner applies meta-cognitive activities
when taking control over- and reflecting on learning.
C. Synthesis of Self-Regulated and Competence-based
Learning
The INNOVRET model aims to leverage the benefits
of both the competence-based and SRL methods. The
CbKST principles are used to adaptively support the
learners in every phase of their SRL process. For that
purpose the CbKST principles are linked to the three SRL-
2014 IEEE 14th International Conference on Advanced Learning Technologies
978-1-4799-4038-7/14 $31.00 © 2014 IEEE
DOI 10.1109/ICALT.2014.28
62
phases. Based on the domain model, the learning content
and the assessment items presented to a learner are
selected according to the learner’s current competence
state (i.e. the set of competencies that a learner
demonstrates). Selected learning content is presented to the
learner in form of recommendations that can be followed
or not. Visualisations of the learner’s competence state and
learning history support reflection and awareness. Given
the fact that all recommendations are based on the
competence state, the learning process shall be efficient
(time and effort) whilst at the same time self-regulated.
D. Learning Process
We define a learning process in accordance with the
SRL model, offering Moodle plug-ins to support the three
separate phases. Each learning process starts with the
selection of a target learning profile (i.e. a set of
competences a learner aims to demonstrate after
completing the learning process) and an initial assessment
of a learner’s competence state. To allow for purposeful
support during the learning process we further introduce
learning iterations (Fig.1). A learning iteration is defined
as the period of time between two complete consecutive
assessments. The learner model is instantiated from the
domain model. Each competence in a learner model has a
probability value that indicates the likelihood to which a
learner may demonstrate a competence. In the learning
phase, LOs are recommended based on probability levels
of the learner model. In this way LOs are selected that
have a medium level of difficulty for the learner.
Competence probabilities in the learner model are updated
with results of every assessment. This is done by applying
the Simplified Updating Rule [8] in a learner’s learning
profile with positive values for correctly answered
questions and negative values for incorrect answers. The
newly updated learner model serves as a basis for the next
learning iteration.
III. CONCEPTUAL AND TECHNICAL APPROACH
This section describes how web services and plug-ins
based on the CbKST have been designed and adapted to
provide a personalised learning environment in Moodle.
The CbKST service provides the algorithms for LO
recommendations and competence assessments including
learner and domain models and exposes them via a REST
interface.
A. Support in the Planning Phase
The planning phase in SRL includes strategic planning
and goal setting (“Forethought Phase” [7]). According to
[2] goal setting can be implemented by defining a set of
competences which are expected to be achieved
(competence goal) or by defining a set of problems which
Figure 1: Learning Process consisting of n Learning Iterations
a learner should be capable of solving. In our approach,
the planning phase is supported by the selection of a
predefined learning profile and an initial competence
assessment. After completing the planning the learner is
forwarded to the plug-in’s main menu (Fig. 2) where links
are presented to engage with learning recommendations,
to complete an assessment, and to reflect on one’s
learning progress. Functions accessible through this
graphical user interface (GUI) align with the definition of
a learning iteration. Initially the learner engages with
learning activities and by this means, enters the learning
phase.
Figure 2: Main Menu - where the learners engage in learning
iterations
B. Support in the Learning Phase
LOs are selected based on a learner’s competence
state. The CbKST service recommends those LOs which
are neither too easy nor too difficult for the individual
learner. This is integrated with Self-Regulated Learning
(SRL) as a combination of guidance (through
recommended learning objects (LOs)) and self-regulation
(through the free selection of LOs). Whenever learners
feel confident about their learning progress they may
complete an assessment and with that leave the learning
phase and enter the reflection phase.
C. Support in the Reflection Phase
In the reflection phase performance data structured by
learning iterations is presented to the learner. At the end
of a learning iteration an assessment confronts the learner
with questions related to content of the learning iteration.
Resulting performance values update the learner model’s
probabilistic values. Metrics illustrated to the learners
report on the learning objects which have been accessed,
the results of the assessment questions and the learner’s
progress within the targeted learning profile (learner
model values).
IV. EVALUATION
An evaluation of the software was conducted with
fourteen male adolescents employed in heat pump
installation in Ireland. These participants were organised
in two groups: one group (the control group) had access to
training material via Moodle; the other group (the
experimental group) used Moodle extended with learning
support tools built on CbKST. The learning content
63
covered technical knowledge about heat pumps, their
installation, and maintenance. After the learning phase
they filled out a questionnaire on their learning and
system experiences (Fig. 3).
Figure 3: The mean values of the CbKST group (N=6) and the Moodle
group (N=8) of the evaluation questionnaire.
Three of the questions targeting the overall approach,
namely the iterative learning process (Q1), the awareness
support (Q2), and the guidance support (Q4), were
answered above average. Two negatively posed questions
concerned learning problems, namely: if this approach
was limiting (Q3) or stressful (Q5). According to the
answers the CbKST approach is less limiting. Further
questions targeted the participants’ enjoyment (Q6) and
the perceived learning success (Q7), which were both
above average and better than for the control group. The
user interface (Q8) and the content quality (Q10) were
above average and similar to the control group. The
question, if the users would like to use a system like this
in the future (Q9) resulted clearly above average and was
considerably better than for the control group.
According to observations of the CbKST group by
the researcher revealed that installers whose IT skills are
good or average did not have any problems navigating,
using, and interacting with the system, whereas installers
who are not confident in using computers found the
system itself to be a barrier. This fact is also visible in the
rather high standard deviation values between 0.98 and
1.47 for the ten questions. In interviews the participants
with proper IT skills stated that the CbKST approach
would be good and efficient. However, they found that
there is room for improvement regarding the simplicity of
the user interface. According to the log data of the CbKST
group the participants, on average have performed 3.2
learning iterations, visited 9.4 learning objects, followed
82% of the recommended learning objects, and answered
9.2 assessment questions.
V. CONCLUSION
In this paper we introduced Moodle plugins to
adaptively support and guide learners through a learning
domain while trying to promote self-regulation. The
approach was developed and implemented within the
INNOVRET project that specifically targets the student
group of heat pump installers in distance learning settings.
It is expected that their knowledge (or lack thereof) and
goals vary greatly, and so teaching content to the same
extent and in the same order to every student would not
make sense. However, a generally valid domain model
allows the generation of learner models that serve as basis
for personalised guidance (LO recommendation) and
adaptive assessment. The recommendation of LOs tailored
to a learner’s knowledge, is time and energy saving, as
well as efficient and motivating. This can lead to the so-
called ‘flow experience’ [9], wherein frustration (caused
by over-challenging LOs) and boredom (caused by less
challenging LOs) are avoided.
A first evaluation with the project’s target group (heat
pump installers) left us with quite encouraging results as
the CbKST group rated their satisfaction with the system
above average and on essential scales higher than our
control group.
ACKNOWLEDGMENT
We would like to thank the European Commission for
funding the INNOVRET project (www.innovret.com,
http://www.adam-urope.eu/prj/8997/project_8997_en.pdf)
within the Leonardo da Vinci Transfer of Innovation
Programme.
REFERENCES
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This paper provides an overview of the linkage of two pedagogical approaches, Skills-based Learning and Self-Regulated Learning (SRL) supported by software. In linking these approaches, adaptive skills-based learning tools based on a psycho-pedagogical competence model, were assigned to a cyclic SRL process-model. The INNOVRET project provides the underlying framework in which this new model will be tested. Software components for Moodle (the learning management system of choice) were specifically tailored to suit the needs of the project's targeted audience (i.e. heat pump installers). The entire system/model will be critically analysed in this paper.
Article
Competence-based Knowledge Space Theory (CbK5T) has been proven to be a very well-fitting basis for realizing personalization in technology-enhanced learning. Especially in the area of game-based learning, however, some extensions and improvements are needed. Personalization in a serious game cannot be regarded simply as the selection of game assets according to the individual learner's current competences but it must also pay heed to the up-keeping of a storyline, it must be ensured that no part of the story is omitted that may be necessary to understand a later part. Therefore, a CbKST-compatible Markovian model for storytelling is proposed. A second issue is the ongoing, non-invasive assessment of the learner's current competences during the game. Every action of the learner within the game should be taken into account for the competence assessment, and the assessment must be done in real-time, i.e. there must not be any delay caused by the assessment which would interrupt the flow of the game. A simplified update procedure for competence assessment within CbKST is suggested which can solve this issue, and simulation results are presented comparing the new procedure with the classical one.